In previous machine learning based fast intra mode decision algorithms for screen content videos, feature design is a key task and it is always difficult to obtain distinguishable features. In this paper, the idea of interaction of features is introduced to fast video coding algorithm, and a fast intra mode decision algorithm based on feature cross is proposed for screen content videos. The numeric features and category features are designed based on the characteristics of screen content videos, and the adaptive factorization network (AFN) is improved and adopted to carry out feature interaction to designed features, and output distinguishable features. The experimental results show that for AI (All Intra) configuration, compared with standard VVC/H.266, the coding time is reduced by 29.64% and the BD rate is increased only by 1.65%.

The latest versatile video coding (VVC) introduces some novel techniques such as quadtree with nested multi-type tree (QTMT), multiple transform selection (MTS) and multiple reference line (MRL). These tools improve compression efficiency compared with the previous standard H.265/HEVC, but they suffer from very high computational complexity. One of the most time-consuming parts of VVC intra coding is the coding tree unit (CTU) structure decision. In this paper, we propose a low-complexity multi-type tree (MT) pruning method for VVC intra coding. This method consists of lookahead search and MT pruning. The lookahead search process is performed to derive the approximate rate-distortion (RD) cost of each MT node at depth 2 or 3. Subsequently, the improbable MT nodes are pruned by different strategies under different cost errors. These strategies are designed according to the priority of the node. Experimental results show that the overall proposed algorithm can achieve 47.15% time saving with only 0.93% Bjøntegaard delta bit rate (BDBR) increase over natural scene sequences, and 45.39% time saving with 1.55% BDBR increase over screen content sequences, compared with the VVC reference software VTM 10.0. Such results demonstrate that our method achieves a good trade-off between computational complexity and compression quality compared to recent methods.

Virtual Reality (VR) 360 degree video has ultra-high definition. Reducing the coding complexity becomes a key consideration in coding algorithm design. In this paper, a novel candidate mode pruning process is introduced between Rough Mode Decision and Most Probable Mode based on the statistical analysis of the intra-coding parameters used in VR 360 degree video coding under Cubemap projection (CMP) format. In addition, updated coding bits thresholds for VR 360 degree video are designed in the proposed algorithm. The experimental results show that the proposed algorithm brings 38.73% and 23.70% saving in average coding time at the cost of only 1.4% and 2.1% Bjontegaard delta rate increase in All-Intra mode and Randomaccess mode, respectively.

In this letter, fast intra mode decision algorithms for HEVC Screen Contents Coding (SCC) are proposed. HEVC SCC has been developed to efficiently code mixed contents consisting of natural video, graphics, and texts. Comparing to HEVC version 1, the SCC encoding complexity significantly increases due to the newly added intra block copy mode. To reduce the heavy encoding complexity, the evaluation orders of multiple intra modes are rearranged and several early termination schemes based on intermediate coding information are developed. Based on our evaluation, it is found that the proposed method can achieve encoding time reduction of 13∼30% with marginal coding gain or loss, compared with HEVC SCC test model 2.0 in all intra (AI) case.

The current high efficiency video coding (HEVC) standard is developed to achieve greatly improved compression performance compared with the previous coding standard H.264/AVC. It adopts a quadtree based picture partition structure to flexibility signal various texture characteristics of images. However, this results in a dramatic increase in computational complexity, which obstructs HEVC in real-time application. To alleviate this problem, we propose a fast coding unit (CU) size decision algorithm in HEVC intra coding based on consideration of the depth level of neighboring CUs, distribution of rate distortion (RD) value and distribution of residual data. Experimental results demonstrate that the proposed algorithm can achieve up to 60% time reduction with negligible RD performance loss.

This paper presents an adaptive transform coefficient scan method that effectively improves intra coding efficiency of H.264. Instead of applying one zig-zag scan to all transform blocks, the proposed method applies a field scan to a horizontally predicted block, a horizontal scan to a vertically predicted block, and a zig-zag scan to blocks predicted in other prediction modes. Experiments based on JM9.6 were performed using only intra coding. Results of the experiments show that the proposed method yields an average PSNR enhancement of 0.16 dB and a maximum PSNR enhancement of 0.31 dB over the current H.264 using zig-zag scan.